In a Global Positioning System (GPS), satellites orbiting the earth transmit signals to passive receivers on the ground. The receivers only receive signals, but they do not transmit signals. One limitation of GPS receivers is that they require an unobstructed view of the sky. As a result, GPS receivers typically are better suited for outdoor use and in areas away from tall buildings or heavy tree cover. A further limitation of GPS location devices is their dependence on an accurate external time reference.
In a GPS system, each of many GPS satellite transmits a signal that includes data to indicate the satellite's location and current time. GPS systems use two carrier frequencies (L1 and L2) for transmitting information, including satellite location, ionospheric propagation delays, offsets between satellite clock time and true GPS time. Additionally, GPS measurements are determined from pseudoranges, which are range measurements biased by receiver and satellite clock errors. The GPS satellites are all synchronized to transmit repeating signals at the same time. Because each satellite is located at a different distance from a receiver on the ground, transmitted signals arrive at the GPS receiver at slightly different times. The receiver uses the different receipt times for various signals to calculate the receiver's location in three dimensions.
U.S. Pat. Nos. 5,953,683; 7,143,004; and 7,533,569 describe sourceless orientation sensors. For example, U.S. Pat. No. 7,533,569 discloses a method of measuring positional changes of an object by using multiple accelerometers. U.S. Pat. No. 7,236,091 describes a hybrid RF/inertial position tracking system having a “wide resolution” mode for general position tracking, and a “high-resolution” mode that employs kinematic models. In this system, the high-resolution position accuracy is considered to be within the order of meters. U.S. Pat. Nos. 7,409,290; 6,167,347; 6,292,750; 6,417,802; 6,496,778; 5,923,286; 6,630,904; 6,721,657; 7,193,559; and 6,697,736 describe GPS-aided positioning and navigation methods. For example, U.S. Pat. No. 7,409,290 altitude and heading information are used to aid the GPS positioning when satellite signals are not available.
Unlike GPS, where transmission time is measured from a satellite to a mobile device or receiver, high-accuracy systems that track mobile devices in three dimensional space measure the time that a signal arrives from the mobile device to a system's connected (either wired or wireless) antennae. These systems do not have the bias errors that GPS has. These time-based, high-accuracy RF positioning systems that use networked antennae for comparing signal time of arrival or difference of arrival measurements consist of receiver hardware having multiple receiver antennae and transmitter hardware having one or more transmitter antennae. To track a single transmitter or transmitter antenna in three dimensions, at least four receiver antennae are required. Similarly, for tracking in two dimensions, at least three receiver antennae are required.
Also unlike GPS, where the tracking calculation is performed in the mobile device, the RF system's receiver antennae provide the reference frame in which the mobile antennae are tracked. More receiver antennae provide better coverage and more accuracy, but do so with increased complexity and cost. The receiver antennae must be distinct, fixed, and have a known location in space. More transmitter antennae attached to or embedded in a tracked object allow the object's orientation to be calculated based on geometric principles. For example, two transmitter antennae, separated by a distance D, yield a pointer, since the two transmitter antennae form a line with known direction. Three transmitter antennae provide enough information to calculate three dimensional position and orientation. The system can be reversed, with the receiver antennae being tracked and the transmitter antennae providing the reference frame.
The major source of error in RF positioning systems is signal propagation errors, such as multipath. While many methods have attempted to mitigate this problem (antennae diversity, spread spectrum), signal propagation errors are very difficult to totally eliminate. A sourceless navigation system does not have these issues, but does have its own set of problems. Sourceless navigation systems are typically based on inertial sensors, which can consist of accelerometers and gyroscopes, as well as magnetic sensors. The use of small inertial sensors, like gyroscopes and accelerometers, has become commonplace in position tracking. Inertial sensors overcome problems like line-of-sight restrictions that plague tracking systems. Unfortunately, commercial, low-cost devices have drift, bias and scale factor errors and orientation motion and positional motion need to be algorithmically separated.
A positioning solution is obtained by numerically solving Newton's equations of motion using measurements of forces and rotation rates obtained from the inertial sensors. The magnetic sensor helps to define azimuth based on the earth's magnetic field. The accelerometer, gyroscope, and magnetic sensor, and various combinations thereof, together with the associated hardware and electronics comprise the inertial/magnetic devices subsystem (IMDS).
Angular orientation may be determined by integrating the output from angular rate sensors. A relatively small offset error on the gyroscope signal will introduce large integration errors. Accelerometers measure the vector sum of acceleration of the sensor and the gravitational acceleration (g). In most situations, g is dominant, thus providing inclination information that can be used to correct the drifted orientation estimate from gyroscopes. The principles for measuring orientation of a moving body segment fusing gyroscopes and accelerometers in a Kalman filter have been described in H. J. Luinge, Inertial Sensing of Human Movement (Ph.D. Thesis, 2002), and is incorporated by reference herein in its entirety. The magnetic sensor is sensitive to the earth's magnetic field and it gives information about the heading direction in order to correct drift of the gyroscope about the vertical axis. Methods for integrating these devices are described in E. R. Bachman, Inertial and Magnetic Tracking of Limb Segment Orientation for Inserting Humans into Synthetic Environments (Ph.D. Thesis 2000), and E. Foxlin, Inertial Head-Tracker Sensor Fusion by a Complementary Separate-Bias Kalman Filter Proc. of VRAIS '96, 185-94 (1996), both incorporated in their entireties by reference herein.
These Kalman filter implementations use accelerometers and magnetic sensors for low frequency components of the orientation and use gyroscopes to measure faster changes in orientation. Finally, an accelerometer-only based position and orientation tracker is disclosed in “Design and Error Analysis of Accelerometer-Based Inertial Navigation Systems,” by Chin-Woo Tan Sungsu Park for the California Partners for Advanced Transit and Highways (PATH).
Methods for integrating similar IMDS components with GPS, acoustic, optical and magnetic tracking systems are known in the art. Some examples include “Robust Dynamic Orientation Sensing Using Accelerometers: Model-based Methods for Head Tracking in AR”, by Matthew Stuart Keir, “Accelerometer-based Orientation Sensing for Head Tracking in AR & Robotics,” by Matthew S. Keir, et al, “Using Gravity to Estimate Accelerometer Orientation,” by David Mizell, “Setting up the MMA 7660FC to do Orientation Detection,” Freescale Semiconductor AN3840, “3D Orientation Tracking Based on Unscented Kalman Filtering of Accelerometer and Magnetometer Data,” Benoit Huyghea et al., “Inertial and Magnetic Sensing of Human Movement near Ferromagnetic Materials,” Daniel Roetenberg et al., “An Extended Kalman Filter for Quaternion-Based Orientation Estimation Using MARG Sensors,” Joao Luis Marins et al., “An Improved Quaternion-Based Filtering Algorithm for Real-Time Tracking of Human Limb Segment Motions using Sourceless Sensors,” Eric Bachmann et al., and are incorporated by reference herein in their entireties. In addition to these patents, the general methods for incorporating GPS and sourceless sensors are described in “The Global Positioning System & Inertial Navigation,” by J. Farrell and M. Barth, (McGraw-Hill 1999); “Global Positioning Systems, Inertial Navigation and Integration,” by M. Grewal, L. Weill, and A. Andrew, (John Wiley and Sons 2001); and “Introduction to Random Signals and Applied Kalman Filtering,” by R. Brown and P. Hwang (John Wiley & Sons 1983). These references are also incorporated by reference in their entireties.